A New Interpretation of the Logistic Model in Estimating Seasonal and Yearly Natural Gas Consumption

Abstract

The logistic type model, which requires the use of an optimization routine for the estimation of model parameters, is one of a number of widely used methods in modeling natural gas consumption. In this study, we derive a regression model by considering the Euler discretization of the logistic type model and show that this regression based approach offers a simpler estimation procedure, in addition to better modeling natural gas consumption data. For comparison, the regression model derived from the logistic model is fitted to the same dataset published in Forouzanfar et al. (2010). Furthermore, in the regression approach, confidence intervals for point forecasts can be obtained, whereas the logistic function model is a deterministic function of time that does not provide confidence intervals.

Daily natural gas consumption forecasting based on a structure-calibrated support vector regression approach

“…method, Goncu [6] established a logistic-based approach, and Wu et al [7] proposed a new grey model to forecast yearly NG consumption. For monthly basis, Kizilaslan and Karlik [8] used a hybrid neural network method, and Karimi and Dastranj [9] applied artificial neural networks to estimate the monthly NG consumption.…”